The method subtracts a constant value δ, called a discounting coefficient, from all the observed term frequencies and estimates the probability of t i as: Note that the discounting effect is stronger for low-frequency terms.

we sometimes use f req(  The view of the co-occurrence matrix can be further extended by assigning probabilities to each cell.


It automatically built and incrementally extended a BN from propositions read in a story, so that the BN represented hypotheses that became plausible as the story unfolded.

for each of these operations, the message announces how many times the operation was performed (e.g., how many nodes were deleted) and then provides sufficient information to enable the message receiver to identify the targets of the operation (e.g., which nodes were deleted).


The goodness of a wordmatch depends on the following factors: (1) level of synonymy -the number of synonyms the words have in common (according to WordNet, Miller et al., 1990); (2) position in sentence; and (3) partof-speech (PoS) -obtained using MINIPAR (Lin, 1998).

we approximate Pr   Our system generates candidate interpretations for an argument by first postulating propositions that match the sentences in the argument, and then finding different ways to connect these propositionseach variant is a candidate interpretation.


Now, in our context, Arg contains the argument, and SysInt an interpretation generated by our system.

any representation that supports the generation of a connected argument involving a given set of propositions would be appropriate.


For utterance (3'), almost the same criteria are applied, but the aspect given to variable as it has many instances.

the chart realized from this logical form is the column chart of Figure 1-(1).


In addition, dimensions such as visual prompts should be incorporated.

when two independent variables, and is forbidden, for example.


For some rules there may be no contextual dependencies.

as usual, tradeoff would occur between recall and precision.


It is difficult to identify unknown words in a text since all Chinese characters can either be a morpheme or a word and there are no blank to mark word boundaries.

once the syntactic category of an unknown word is known its contextual bi-gram will be checked.


The question arises as to whether words such as cutlery should be in the upper or lower level.

there were some problems with the granularity of the hierarchy.


Even with a limited ontology and noisy lexicon, semantics does predict countability around 78% of the time.

for example, the NCP associated with the semantic class 910:tableware is uncountable.


(2001) have proposed scoring functions to integrate heterogeneous features.

since National has a typical newspaper style, the beginning of the document was important.


Each sample is represented by a long binary vector, i.e., a sequence of 0 (false) and 1 (true).

we present a method that makes the system substantially faster.


• At present, the techniques of parsing and semantics analysis of the Chinese language are far below the level needed for application.

a method is needed to prevent some orthographic expressions from being automatically replaced with wild cards.


The knowledge useful for WSD can be learned from corpora.

unlike the previous methods using bilingual corpora, our method does not require parallel corpora.


This is another case where RPD and RP are separated.

we manually applied our proposed method to the QDC.


Moreover, this does not work if the following word is a dierent word regardless of recognition results.

for example, we can interpret the utterance, \I, I go there", as a repetition (addition), a restart, or a hesitation or misrecognition of the speech recognizer.


Type 4: Head omission Two examples of this type were found, and both were almost the same expressions used by the same person.

the second instruction is an elaboration of the rst one; namely, it instructs the camera to go around to the right side of the object to shoot it.


It should be useful, when context cannot be used as a symmetry axis, to refine the context with a conceptual vector which is considered as the referent.

it would be a mistake to consider that two synonyms would be at a distance of about π 2 .


We note d the distance between two nodes A and B in a graph.

10% 20% 30% 40% 50% We notice that we obtain, from 10% of the corpus, a 75% precision ratio (3 sentences out of 4 are relevant) and nearly a 50% recall ratio.


His analysis has to deal with the notion of context: he proposes to associate to a word a core semantic description (the fact that a "door" is an "aperture") and to add some additional features, which can be activated in context ("walk-through" is the telic role of a "door").

note that d(sell, \Sell) = d(\Sell, sell) = 1. h(f) = the set of ancestors of f .


The measure is called activation and only takes into account the common features between two nodes in the graph.

as a consequence, we have: the concept \Universe has a daughter \Flower that belongs to h(florist) but not to h(seller).


Although STs already capture the aspect dependency induced by their varying roots, the path criterion is still not met.

we experimented with equal weights of both input vectors.


Text representation is a central task for any approach to automatic learning from texts.

: the more two texts share semantically similar words, the higher the probability that they deal with similar topics.


• We would like to expand the generation rules.


For almost all sets, context word vector 2 provides higher precision than context word vector 1.

its translation price competition has more co-occurring words related to these words than the irrelevant word price control.


First, we collect the translation candidates of a target compound, and then measure the similarity between them to choose an appropriate candidate.

so it is quite difficult to add them and their translations to a dictionary.


Again, though, we are interested in practical performance.

the first of these, termed the Greedy method, is a straightforward steepest-descent algorithm which employs incremental MML calculation to optimise a PtA.


When a symbol is found that does not match a valid transition, the PTA is augmented with a new path to accept the rest of the string.

in this case the PFSA had a maximum of 20 states with an alphabet cardinality of 8.


To measure the quality of the inferred DTD, we use the concept of Minimum Message Length (MML) (Georgeff & Wallace 1984).

the ACO algorithm's failure on large testing data led to a new method which we have named sk-ANt.


In recent years, there has been a burst of projects based on this paradigm.


Running an MT system on test data can be done automatically and requires no additional human language knowledge, but it requires additional time (which can be substantial for MT).

table 2 shows the performance in terms of Precision (P), Recall (R) and F-measure (F).


The selection accuracy was measured using 5-fold cross validation for each grammatical relation.

we evaluated the accuracy performance of each grammatical relation.


The sentence is one of the 500 sentences provided by John Dowding.

to earlier approaches to disambiguation for UBGs, our approach can be effectively applied in practice, enables unsupervised training on free text corpora, as well as efficient disambiguation, and is mathematically clean.


Each bar corresponds to a selection system.

the proposed method scores translation t a in ten ways, T 0 (s; t a ); T 1 (s; t a ); 1 1 1 ; T 9 (s; t a ), when the translations are scored by using a translation model.


In their model, the search scope for possible antecedents was limited to the sentence containing zero pronouns.

here, the average number of antecedent candidates per zero pronoun was 27 regardless of the model, and thus the accuracy was 3.7% in the case where the system randomly selected antecedents.


Section 3 evaluates our method by way of experiments using newspaper articles.

it is possible to estimate the probability based on cooccurrences of verbs and their case nouns, which can be extracted automatically from large-scale unannotated corpora.


This indicates that a high accuracy can be obtained by a relatively small number of supervised articles.

since estimating a semantic model, P (n i |v, c), needs large-scale annotated corpora, the data sparseness problem is crucial.


In analytical languages, the word order is usually taken as rather fixed and that is why it can be employed in parsing tree pruning algorithms.

first, the acquisition of training data set derived by exploitation of a standard dependency tree bank for Czech is described.


The remaining constituent na mzdu (for payroll) is finally recommended as a modifier of the preceding noun phrase záloze ([about the] advance).

the obstacles brought by the synthetical languages in relationship with those simple statistical techniques are indispensable.


For PER type, the context tokens are likely to be person titles and action words.

chinese NE (Named Entity) recognition is a difficult problem because of the uncertainty in word segmentation and flexibility in language structure.


Average word length and density are similar in all three corpora.

social bonding is performed by adapting to a common interaction style (Brown and Levinson, 1987;Okada et al., 1999).


Similarly, suppose X denotes a set of intervals of manually annotated tags for a document and Y denotes a set of intervals of automatically annotated tags for the same document.

we used the similarity measure calculation as the central computing mechanism for inferring the relevance between the XML tags and tags specified in the TIQL/interval operation, determining the most relevant tags in the XML-based KS(s).


MeSH Term in MEDLINE (2002) and Gene Ontology (2002) provide a top-down controlled ontology framework, which aims to describe and constrain the terminology in the domain of molecular biology.

table 2: tIMS -practical performance table 2 and Figure 8 show the results: t he process ing times of tIMS were about 1.4-1.8 times faster (depending on number of tags and corpus length) than those of REM.


These results demonstrate that this approach can scale up to a large real-world task and show a lot of potential for text classification.

this label is determined by the most significant weights of topics in an input vector based on one of the above methods.


We extract suitable relations from WordNet to present a semantic map of news articles and show that these relations can complement neural techniques in text categorization.

people can choose the relevant clusters of documents on the map to get relevant documents.


Memory-based Learning stores the training examples and classifies new examples by choosing the most frequent classification among training examples which are closest to a new example.

(2000) showed that the result combined by seven different machine learning algorithms outperformed the best individual result.


The linguistic approach is more laborious but the end result can be more user friendly.

the experimental result is presented in Section 3.


The tree descending algorithm, a technique widely used for text categorization, proved to be much less efficient than standard classifiers when applied to the task of augmenting a domain-specific thesaurus.

we believe that a study of the two algorithms on the material of a larger thesaurus, where richer taxonomic information is available, can yield a further understanding of its role in the performance of the algorithms.


Out of the 756 concepts, 182 were non-final; correspondingly, 182 word classes were formed.

at the very beginning of the search down the tree, a very large portion of test words was found to be similar to such concepts.


We are grateful to Jürgen Trouvain for kindly making his corpus available to us, and to three anonymous reviewers for their comments.

the results are not quite satisfactory yet, because some of the steps of the overall procedure for assigning phrase breaks were manually corrected.


This is expected, since the modifier penalties make this model more conservative in proposing modifier candidates.

here, we report of further improvements to this model that we obtained by adding a class-based bigram term to head prediction.


Then, each sense of a target word can be represented as a sense vector, which is the centroid of the context vectors in word space.

the values, we used, are  the results were evaluated by precision rates (Salton, et al., 1983).


It is called multilingual voice transcription and described later.

the multilingual speech identification has a large part to play with unknown-language speech input.


The cost of a parse tree can be calculated by adding the cost of lenient default unification, which is the amount of information that is lost by lenient default unification.

the problem in Carpenter's default unification is that it tries to maximize the amount of information in a default feature structure, not the result of default unification.


By Mock (1996), terms occurred in the title have higher weights.

the similarity value between title t and sentence S i in a document d is calculated by the following formula: where t r denotes a vector of the title, and i S r denotes a vector of a sentence.


In this paper, we use two kinds of text summarization techniques for classifying important sentences and unimportant sentences.

we obtained the best performance at 1.5 (k 1 ) and 0.4 (k 2 ) for SVM: 1.9 and 3.0 for Naïve Bayes, 2.0 and 0.0 for Rocchio, and 0.8 and 2.8 for k-NN.


Instructions / number of words for Chink & Chunk (logarithmic scale) With the shallow parser algorithm, we can detect and label more syntactic and hierarchic data: in the average, worst a nd best cases, for M sentences, each sentence consisting of Nw words; for a set of C precompiled categories, its complexity has an order of M*C*(Nw²+Nw)*Constant.

mandatory cooccurrence between sets of categories Uniqueness (Uniq) Set of categories which cannot be repeated in a phrase In this approach, describing a phrase consists in specifying a set of constraints over some categories that can constitute it.


The standard for assigning the intention tags was established by extending the decision tree proposed as a dialogue tag scheme (JDRI, 2000).

an input utterance might also receive the influence in the contents of the speeches before it.


This algorithm has been built into an on-line demonstration where the user inputs a given seed word and can then see the cluster of related words being gradually assembled.

• The equivalence classes of the graph G under this relation are called the components of G. We are now in a position to define the senses of a word as represented by a particular graph.


Then the saliency of w in D(T) is defined as −log(hgs(N, K, n, k)) ２ .

sAL(D(T), s) is the number of distinct words in D(T) whose saliency of occurrence is not less than s. For instance, using the 1996 archive of Nihon Keizai shimbun (a Japanese financial newspaper), sAL(D("Aum ３ "), 110) = 74, sAL(D("Aum"), 200) = 50, sAL(D*("do"), 110) = 1, and sAL(D*("do"), 200) = 0, where D*("do") is a set of N 0 randomly chosen articles from D("do") and N 0 is the threshold value stated in subsection 1.2.


In this paper, we provide machinery to reduce the amount of human effort needed to adapt existing models to new corpora: first, we propose a flexible notation for specifying these rules that would allow them to be shared by different models; second, we report on an experiment to see whether we can use Expectation-Maximization to automatically fine-tune a set of handwritten rules to a particular corpus.

for Penn Treebank II style annotation (Marcus et al., 1993), in which a nonterminal symbol is a category together with zero or more functional tags, we adopt the following scheme: the atomic pattern a matches any label with category a or functional tag a; moreover, we define Boolean operators ∧, ∨, and ¬.


In addition, we make the ∪ operator noncommutative: α ∪ β tries to match α first, and β only if it does not match α, as in Perl.

furthermore, it is not at all apparent that tree-transforming heuristics that are useful for one parsing model will be useful for another.


We first trained the initial model on sections 02-21 of the WSJ corpus using the original head rules, and then ran the Inside-Outside algorithm on the same data.

since the resulting model is quite large, if an event at a particular point in the derivation forest has an expected count below 10 −15 , we throw it out.


Suppose the author next chooses to expand admin(A); if the system was working in a naive fashion, it would then display the choices swallow, chew, and drink.


MacDonald (1993) also uses hand-written rules for coarse named entity categorization.

further, unlike the case with location names, exhaustive lists of person names by category do not exist and cannot be relied upon for training and test set generation.


The head words of the resulting subject and object nodes were found using the deterministic headword rules employed by the parsing model.

this observation motivated a new probabilistic model of verb argument structure designed to explicitly capture alternation behavior.


The individual observations of our dataset are noun-verb pairs of three types: direct object, subject of a verb with an object, and subject of a verb without an object.

acknowledgments This work was undertaken with funding from the Institute for Research in Cognitive Science at the University of Pennsylvania and DoD Grant MDa904-00C-2136.


As the summary of discussions in the previous two subsections, the essential idea of this new model is in two aspects: • Creating titles from the distilled 'information source'.

as already discussed in previous sections, the problem of over-favouring common title words in the old framework can be attributed to the use of term P(T) for the title word ordering phase.


On the other side, a title T with a set of common words can have a good chance to be a title for some documents even its word order is problematic.

a word sequence T with a set of rare words will have small chance to be used as a title for any document even if the word order in T is perfectly correct.


Title generation is a complex task involving both natural language understanding and natural language synthesis.

of course, the probability for the word sequence T to be used as a title for any document is definitely influenced by the correctness of the word order in T. the factor whether the words in the sequence T are common words or not will also have great influence on the chance of seeing the sequence T as a title.


As the result, using probability P(T) for the purpose of ordering title words can cause the generated titles to include unrelated common title words.

title-word-document-word 'translation' probability P(tw|dw) can be learned from the training corpus using statistical translation model (Brown et al., 1990).


An overall probability is attained for each candidate reading using the naive Bayes model over these component probabilities.

this can be a painstaking process due to the large number of characters taking on identical readings, resulting in large lists of characters for the user to choose from.


The resulting readings and their scores are stored in the system database to be queried as necessary.

as of yet, we have not evaluated this part of the system and will not discuss it in detail.


drss are "boxed" first order logic formulae.

two cases arise: • either a condition in the drs appears as a condition in one of the sub-drs; that is the case for condition 1 which appears in the sub-drs labelled π 1 ; • or it is expressed through a discourse relation; that is the case for condition 3 with R 1 (π 1 , π 2 ) → condition 3 , which means that R 1 (π 1 , π 2 ) must have condition 3 among its consequences: no other element is in charge of expressing condition 3 .


The maximum entropy system performed competitively with the best systems on the English verbs in SENSEVAL-1 and SENSEVAL-2 (Dang and Palmer, 2002).

the annotation guidelines are not exactly the same as for the Penn CtB, and can be found at http://icl.pku.edu.cn/research/corpus/coprus-annotation.htm.


Test set S 3566 has a higher score for all algorithms because it has a higher number of average features per word than S 13403 .

by indexing the features, one can retrieve the set of elements that have a given feature.


The word dog belongs to both the Person and Animal classes.

the first cluster is clearly a cluster of firearms and the second one is of pests.


For example, in one experiment, CBC outperforms K-means by 4.25%.

in the newspaper corpus, the Person sense of dog is at best extremely rare.


The example in 3 shows how complicated the structure of a simple sentence "they seem to understand" can be.

the semantic relation between the subject and the verb is not uniform across all verbs, or even for different instances of the same verb.


In our experiments we used two different dictionaries.

finally, for the two learning-based segmentation algorithms, we used two separate training corpora but a common test corpus to evaluate segmentation accuracy.


A common presumption is that word segmentation accuracy should monotonically influence subsequent retrieval performance (Palmer and Burger, 1997).

for the the selfsupervised segmenter we used 10M of data from the data set used in (Ge et al., 1999), which contains one year of People's Daily news service stories.


Super cially, this looks like a small gain.

most of the work to date in this eld has been related to mobile phones.


Indeed, all the reformulation questions that we exemplified in Sec.

the classifiers will be experimented with different feature sets to test the influence of different features.


For the sake of the experimental comparison, we define six feature sets, each of which is an incremental combination of the primitive feature types.

we define where m is the total number of test examples.


The third dataset "all" mixes all the sentences.

we resort to the following approximation method instead.


We construct a belief network that represents the constraints existing among R's and E's.

we derive weak classifiers by treating these two learning tasks as shallow text processing problems.


", three keywords are extracted: \Shinjuku", \Chinese noodle" and \meat dumpling".

at rst, the system is evaluated with text input, which is transcription of the collected queries.


He also created the original implementation of the metarule interpreter as part of the XTAG software, from 1993 to 1995, thereafter improved to reach a first stable form as documented in (XTAG Research Group, 1998).

of   for each combination of redistribution and realization alternatives compatible with the subcategorization frame.


Since a multi-dimensional model can be converted to flat and hierarchical models, three classification strategies are possible, i.e., classifying directly based on the multi-dimensional model and classifying with the equivalent flat or hierarchical models.

two parameters involved are the definition of similarity and the number k. While the standard similarity is defined as tf×idf, a variant (0.5+0.5tf/tf max )×idf that performed better in our preliminary experiments, is applied in this work.


The naïve method to classify documents according to a multi-dimensional model is flat-based classification.

the flat-based classification in the multi-dimensional model deals with the finest granularity of classes because all combinations of classes from predefined dimensions are combined to form a large set of classes.


In this experiment, test documents are classified into the most specified classes say D 12 , which are the combinations of two dimensions, D 1 and D 2 .

a news issue in a Thailand newspaper titled "airplanes attacked World Trader Center" can be classified into "social news", "inter-continental", "bad news" in the first, second and third dimensions, respectively.


This makes such nodes represent classes less imprecisely but there are more training data (documents) for these nodes.

the standard naïve Bayesian (NB) is applied as a statistical approach to our text classification in this work.


To cope with these problems, this paper proposes a new framework, called multidimensional framework, for text classification.

with traditional flat and hierarchical category models, the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each set corresponds to a dimension.


A test document was assigned the class that gained the highest score from the classifier applied.

with a large number of classes or a large hierarchy, the problem of sparse training data per class at the lower levels in the hierarchy raises and results in decreasing classification accuracy of lower classes.


7 In contrast, both Buy and Sell involve simulating the entire transaction, include both transfers as well as the done(ct) node.

while frames and FE tags are meaningful to human interpreters, they are not yet suitable for use in natural language understanding.


NLPTK begins to create an NLP front end by generating semantic representations of sample input sentences provided by the programmer.

the lexicon entry of a word lists all possible parts of speech of the word and its subcategorization frames (if any).


For example, when building an NLP system for a handheld platform, a light parser is needed because the memory cannot accommodate a full parser.

this is not possible in our case because weightings of the Minipar trees are not publicly available, and the shallow parser is a rule-based system without confidence information.


For each sentence, the raters were to determine which of the two automatically translated sentences was the better translation of the (unseen) source sentence, assuming that the reference sentence was a perfect translation, with the option of choosing "neither" if the differences were negligible.

when we take 400 random sentences, the difference between the two versions only affects 30% of the sentences (133 or thereabouts) and therefore does not result in a significant difference (p=0.13 in a one tailed t-test).


The results obtained by Schütze and Pedersen (1995) and by Lytinen et al.


Again, the phrasal word translations "move house", "change one's residence", "move to a better place", etc were filtered out, leaving the single word "move", which has a total of 16 senses as a verb in WordNet 1.7.


Our results show that the combination of the models significantly improves results, and that the use of the hierarchical information contained in our thesaurus, UMLS/MeSH, is of primary importance.

table 3: Evaluation of search strategies.


We followed the three-point grading scheme previously developed for the C-STAR consortium, as described in [Levin, L. & al.

we have to develop the analysis from the textual output of an automatic speech recognition module towards the IF and the generation from the IF towards a text-to-speech input text.


In this paper, we discussed the importance of maintainability and documentation in grammar development.

the levels that are relevant here are constituent structure (c-structure) and functional structure (fstructure).


A modularized grammar is "intensionally transparent", as we put it, and thus favours maintainability.

next, XML markup is added to the source LFG grammar via Perl processing.


Obviously, one needs to read more lines of code in this version, and often these lines of code are spread over different places and files.

(the mapping to the annotated-tree representation is not trivial, since c-structure rules make use of regular expressions.)


In this case the case particle is followed by the topic marker.

(1998) used decision trees to estimate the dependency probabilities.


It is a latent variable model in the sense that only the observation sequence is known while the state sequence remains "hidden".

for shallow parsing, we have o , where is the word sequence and is the part-of-speech (POS) sequence, while the "hidden" states are represented as structural tags to bracket and differentiate various categories of phrases.


It also shows that our LSD-DHMM much outperforms other DHMMs due to the modeling of the long state dependence using the variable-length mutual information-based modeling approach in the LSD-DHMM.

the output model of a LSD-DHMM overcomes the strong context independent assumption in the GHMM and becomes observation context dependent.


these two approaches only work well when the number of different information sources is limited.


the structure in Figure 1 maps relatively easily to the inter-paragraph structure ((AB)C) (as opposed to (A(BC))).

a lexical chain is a sequence of semantically related words and can indicate the presence and extent of subtopics in a text.


We provide some preliminary definitions in Section 2.

this paper will assume that FAs are deterministic, although technically, unambiguity is sufficient for our constructions to apply.


Under this worstcase analysis many simple concept classes are unlearnable.

the partially distribution free learning problem we study here is novel.


For example, the source example of Rule 1 in Figure 3 is obtained from a phrase pair of the Japanese verb phrase "furaito (flight) wo yoyaku-suru (reserve)" and the English verb phrase "make a reservation for the flight."

this result indicates that the results of bottom-up generation are not equal to the global optimal solution.


In the current implementation, all rules that contain examples of the same distance are used.


Most example-based MT systems employ phrases or sentences as the unit for examples, so they can translate while considering case relations or idiomatic expressions.

the difference between our method and these methods involves whether modification is applied.


In addition, the search space becomes smaller because the example-based transfer generates syntactically correct candidates for the most appropriate translation.

this method is sequentially combined example-based and statistical MT.


Because the semantic distance used in the similarity definition did not show any significant effect, we need to find another factor to enhance the similarity measure.

we use an N-gram Language Model (NLM) to generate sentence-splitting candidates, and we use the NLM and sentence similarity to select one of the candidates.


Such a failure occurs particularly when an input is long.

the difference between the conditions of using or not using a thesaurus is not so significant.


For disagrees, argued generated from sentence 4, features pse parent -pattern-1 and pse target -pattern-1 would be 1, while for dislikes, noted generated from sentence 5, feature pse parent -pattern-2 would be 1.

unfortunately, this filtering of information through multiple sources (and multiple points of view) complicates the natural language interpretation process because the reader (human or machine) must take into account the biases introduced by this indirection.


However, newswire does not offer direct access to facts, events, and opinions; rather, journalists report what they have experienced, and report on the experiences of others.


As for crossed dependencies, we also compared arc lengths.

we adopted a sentence unit-based definition of discourse segments.


We have developed a coding scheme with high inter-annotator reliability and used that scheme to annotate 135 texts with coherence relations.

the arrowheads of the arcs represent directionality for asymmetrical relations (elaboration) and bidirectionality for symmetrical relations (contrast).


However, e.g., Haliday & Hasan (1976) stress the importance of anaphoric links as a cue for coherence structures.

(a) is the first segment and (c) is the second segment of what is actually one single discourse segment, separated by the intervening discourse segment (b), which is in an attribution relation with (a) (and therefore also with (c), since (a) and (c) are actually one single discourse segment).


In order to track the frequency of crossed dependencies for the coherence structure graph of each text, we counted the minimum number of arcs that would have to be deleted in order to make the coherence structure graph free of crossed dependencies (i.e.

discourse structure relations reflect how the meaning conveyed by one discourse segment relates to the meaning conveyed by another discourse segment (cf.


In order to maintain trees, these accounts distinguish non-anaphoric coherence structures, represented in a tree, and anaphoric coherence structures, which are not subject to tree constraints.

we also assume that contentful coordinating and subordinating conjunctions (cf.


HEY uses a hierarchical semantic parser that is trained on a set of questions together with their corresponding SQL queries.

this change occurs because, in the AtIS domain, 'list' typically occurs in imperative sentences, such as "List all flights."


When we write a report or an explanation on a newly-developed technology for readers including laypersons, it is very important to compose a title that can stimulate their interest in the technology.

it is difficult for inexperienced authors to come up with a phrase meeting the three key points.


The generation of parse forests 3 for the same sentences took 26,840 seconds (1.09 seconds per word).

to most beam search parsing strategies, BitPar is guaranteed to return the most probable analysis, and there is no need to optimise any scoring functions or parameters.


Such parsers are typically limited to the general concept of resource sensitivity -every element in the input needs to be found exactly once -and the ability to require certain categories to dominate a contiguous segment of the input.

the domain in which word order is determined still is the local tree licensed by an ID rule, which is insufficient for a direct encoding of linearization-based HPSG.


It is straightforward to extend this extraction of type-theoretic formulas from fully specified XDG analyses to an extraction of underspecified semantic descriptions from partial XDG analyses.


We intend to go on to produce more complex models of this type like Bayesian Networks, which are easier to use in a more robust setting, e.g.

this ordering on patterns is useful as it allows common prefixes to be merged during minimisation.


The result was an automaton of 350 states and 839 transitions, compared to an initial 2907 patterns.

we also resolved by hand some of the simpler forms of anaphoric reference to individuals to simulate a fuller discourse processing of the texts.


Such filters consist of constraints ruling out patterns that are definitely not useful, for example patterns containing a verb but no arguments or attributes.

the above pattern is converted to the following: It is now easier to understand the pattern as :'A person C who is elected succeeds a person E'.


The top score is removed from the matrix (i.e.

word alignment as links between words in the source language and words in the target language as indicated by the arrows in figure 1.


That article also introduces the Model 6; additionally, state-of-the-art results are presented for the Verbmobil task and the Canadian Hansards task for various configurations.

in this case, the minimum weight edge cover problem is solved by the one-sided MWEC algorithm.


This nominal is assigned lower prominence than the subject pronoun hun (she) in most salience models.

the κstatistics for the pronoun classification was 0.86.


In most applied approaches resolving pronominal anaphors mainly consists in the following steps: 1: determining the anaphoric antecedent domain; 2: choosing the most prominent or salient antecedent among possible candidates.

7 The antecedent domain for APAs or anaphors which can both be IPAs and APAs is U n−1 .


It takes yes when there is an antecedent expression not captured by any other feature, and it is considered irrelevant (question mark value) when there is an antecedent expression defined by another feature.

deictic temporal expressions like 'then' trigger reprise interpretations.


In the distributional similarity scheme each word w is represented by a feature vector, where an entry in the vector corresponds to a feature f. Each feature represents another word (or term) with which w co-occurs, and possibly specifies also the syntactic relation between the two words.

this may yield high similarity scores for less similar word pairs, while missing other correct similarities.


Once RFF weights have been computed they are sufficiently accurate to allow for aggressive feature reduction.

in many cases, high ranking features in a word vector, when sorting the features by their weight, do not seem very characteristic for the word meaning.


Example (6), which is an idiom, shows how each of its constituent parts is interpreted in isolation.

the interpretation "whip" and more rare meanings, "beautiful thing" and "ornamental stitch", are also ruled out.


Some applications such as Information Extraction from biological documents benefit from word dependency analysis even without phrase labels.

, w n /p n ) backward from the end of the sentence, each word w i is classified into three categories: Left, Right, and Shift.


Word dependency is important in parsing technology.

the sentence looks like: The following features of neigbors are also used as w i 's features.


The figures for β = 0 are obtained by assigning all categories to a word from the word's entry in the tag dictionary.

the final row corresponds to a more restrictive setting on the supertagger, in which a value of β = 0.05 is used initially and β = 0.1 is used if the node limit is exceeded.


In essence the signs for boundary tones are similar to the pitch accent signs, except that they do not contain a foc feature in the argument part.

steedman introduces information structure as an integral part of the CCG formalism (steedman, 2000).


While the pure CG only involved functional application rules for combining categories, CCG introduces several additional combinatory rules for both syntactic and semantic composition -forward and backward composition, and crossed composition, as well as substitution rules.

• If X and Y are signs X\Y is a complex sign.


But we think that abstractions should be considered for specific frameworks to be really efficient.

we propose here another procedure adapted to a formalism C with the property of projectivity.


We choose polarization followed by destructuring as abstraction.

parsing with the simplified grammar in the abstract formalism can be used efficiently for filtering lexical selections.


for each German word, this tool provides its base form and the sequence of morphosyntactic tags, and this information is then added into the original corpus.


The recall is more important than the precision since we want discourse relations that are as correct as possible.

such a situation happens when an adverb stands before a strong cue phrase.


Yet, although there has been a tremendous amount of AI research on topics such as control and navigation for robots, the issue of integrating dialogue capabilities into a robot has only recently started to receive attention.

5 In addition to computing the moves that the robot will perform, the chess programme is also used in disambiguating elliptical player inputs.


The robot represents a charismatic Italian shell-game player, and engages a human player in style: Luigi speaks German with a heavy Italian accent, lets the human player win the first round, and then tries to pull several tricks either to cheat or to keep the player interested in the game.

building talking robots is still a challenge that combines the particular problems of dialogue systems and robotics, both of which introduce situations of incomplete information.


The result of case analysis tells if the zero pronouns exist.

the ordered possible antecedents for "ga" are L 7 :"Minsyutou", L 14 :"Jimintou"(φ ga), L 14 :"Ishihara chiji"(φ wo), • • •.


Currently, when asked to output terms associated with medicine, the browser returns some 504 nouns, verbs, and adjectives (both single words and phrases), representing some 270 different senses.

the links among the synsets structure the noun and verb lexica into hierarchies, with noun hierarchies being considerably deeper than those for verbs.


This representation is then used to describe the task of selecting textual units for a summary or answer as a formal optimization task.

this approach is similar to the calculation of recall, precision, and F-measure adopted in the recent NISt evaluation of long answers for definitional questions (Voorhees, 2003).


Table 1 shows the number of data sets for which the adaptive greedy algorithm outperforms our baseline.

obviously, each of the selected text snippets should individually be important.


Partial matches between textual and conceptual units introduce a new problem, however: if two textual units partially cover the same concept, it is not apparent to what extent the coverage overlaps.

both our algorithms operate by first calculating a ranking of the textual units in decreasing order.


Figure 1 illustrates the NTPC architecture.

the high-performing base model is based on AdaBoost.MH (Schapire and Singer, 2000), the multi-class generalization of the original boosting algorithm, which implements boosting on top of decision stump classifiers (decision trees of depth one).


Our original goal was to adapt TBL for error correction of high-performing models (Wu et al., 2004a), with two main principles: (1) since it is not clear that the usual assumptions made about the distribution of the training/test data are valid in such extreme operating ranges, empirical observations would take precedence over theoretical models, which implies that (2) any model would have to be empirically justified by testing on a diverse range of data.

the experiments run in this section were performed on the data sets used in the CoNLL-2002 andCoNLL-2003 Named Entity Recognition shared tasks.


This is expected, since it was one of our goals to reinvent as little as possible.

our resulting error corrector, NTPC, was instead constructed on the principle of making as few assumptions as possible in order to robustly generalize over diverse situations and problems.


This is perhaps not too surprising upon further reflection, since the principle of Occam's Razor does prefer simpler hypotheses over more complex ones.

in an error-correcting situation, most of the samples are already correctly labeled, errors are few and far in between and the sparse data problem is exacerbated.


cally supervised", in fact this supervision comes in the form of the annotation of the Wall Street Journal treebank on which the parser is trained, rather than parses for our parallel training corpus.

it is important to note, however, that both algorithms are unsupervised in that they are not provided any hand-aligned training data.


To this end, we characterize the world of artifacts as being made up of three essential classes: entities which represent the tangible objects themselves, relations which represent how the entities relate to one another, and events which change the state of entities or relations.

its overall architecture, focusing on our contributions to the system is illustrated in Figure 1.


We also demonstrate how it uses the current state of the artifact in the KB to constrain the interpretation of utterances in context, and how multimodal gestures help to resolve ambiguous interpretations.

the DMT has been extended to allow for the same parse to attach in multiple locations-these multiple attachments are eventually pruned as more evidence is accumulated in the form of further speech or gestures-that is, as hypotheses are confirmed or rejected over time.


we set to 1, so this F-measure was averaged over all summary sentences.


Barzilay (Barzilay and Elhadad, 2003) combines edit distance and context information around sentences.

dTPs improved the results of BOW by about five points.


For each sentence in the given abstract, the corresponding source sentence is determined by combing the similarity score and heuristic rules.

for ESK and WSK, we have to choose parameters, .


In this example, we permit " (overseas study)".

our research need to include PoS guessing for the identified words.


From a more qualitative perspective, we clearly saw the benefit of the filtering mechanism in the example in Section 3.3 (Tables 2 and 3): when a gen-eration component is used to describe the contents of clusters, the inclusion of author-specific words is uninformative and even confusing.

this gives an overall measure of how well the cluster represents the newsgroup, taking into account the 'correctness' of the cluster (precision) and how much of the newsgroup it accounts for (recall the F-score is then calculated as have been calculated, we choose for each cluster the best newsgroup assignment, i.e.


O r i g i n a l Q u e r y E x p a n d e d Q u e r y I n i t i a l R e t r i e v a l F i n a l R e t r i e v a l Q u e r y E x p a n s i o n D o c u m e n t S e t I n i t i a l R a n k i n g D o c u m e n t s F i n a l R a n k i n g D o c u m e n t s The rest of this paper is organized as following.

they are both used in a lot of experiments and applications.


The context vectors are accumulated by adding together k-dimensional index vectors that have been assigned to each context in the data -whether document, paragraph, clause, window, or neighboring words.

another refinement method is to use feature extraction, which means that "artificial" features are created from the original ones, either by using clustering methods, such as distributional clustering (Baker and McCallum, 1998), or by using factor analytic methods such as singular value decomposition.


The Random Indexing approach is motivated by the Johnson-Lindenstrauss Lemma (Johnson and Lindenstrauss, 1984), which states that if we project points into a randomly selected subspace of sufficiently high dimensionality, the distances between the points are approximately preserved.

we conclude that concept-based representations constitute a viable supplement to wordbased ones, and that there are categories in the Reuters-21578 collection that benefit from using concept-based representations.


This suggests that BoC representations are more appropriate for large-size categories.

since there are more nearly orthogonal than truly orthogonal directions in a high-dimensional space, choosing random directions gets us sufficiently close to orthogonality to provide an approximation of the unary vectors (Hecht-Nielsen, 1994).


The recall was not so high, especially in the MT method, but according to our error analysis the recall can be increased by adding auxiliary patterns.

users can refer to the original text by clicking on the document icons.


To rule out this scenario, we can sample instances where machine translations are ranked higher than human translations.

s2 is better than s3 and s4, and s4 is better than s3.


Table 2 shows the results for BLEUS1 to 9.

the performance of automatic metrics in terms of human vs. system correlation analysis is not stable across different evaluation settings.


The other is to pronounce by simply pronouncing the core sound of the vowel.

in the system, two language models are equiped; one stands for traditional word recognition, whereas the other one is used for spelling recognition.


on this basis, in TSC3, we assumed that the process of multiple document summarization consists of the following three steps, and we produce a corpus for the evaluation of the system at each of the three steps 4 .


Therefore, we believe that second type of extract is superior and thus we prepared the extracts in that way.

moreover, it is peculiar to multiple document summarization that the summarization system must decide how much redundant information should be deleted 3 .


Step 1 Extract important sentences from a given set of documents Step 2 Minimize redundant sentences from the result of Step 1 Step 3 Rewrite the result of Step 2 to reduce the size of the summary to the specified number of characters or less.

a text summarization system needs to recognize such redundant sentences and reduce the redundancy in the output summary.


As such, soft pattern rules may not meet the precision requirement of the task.

instead of generalizing a rule from a specific instance as is done in most existing pattern rule induction algorithms, GRiD examines the global feature distribution on the whole set of training examples in order to make better decision on rule induction.


During the test phase, both sets of pattern rules are used in a cascaded way, with hard pattern rules followed by soft pattern rules, to extract target slots from new documents.

to the existing work, we propose a weakly supervised IE framework which takes advantages of both soft and hard matching pattern rules in both the training and test phases.


The SVD Key Sentence Method is similar to the preceding method.

to collect a corpus of these threads, we placed a few constraints on the mailing list archives we found online.


Finally, we experimented with using left and right context in the estimation of lexical probabilities, which we refer to as a contextualized HMM.

finally, we noticed that a certain number of frequently occurring words (e.g.


We detect as new words those that are not in the existing word list, yet are either highly confident segments, or low confident segments that are surrounded by high confident words.

a traditional maximum entropy tagger, as used in Xue (2003), labels characters without considering dependencies among the predicted segmentation labels that is inherent in the state transitions of finitestate sequence models.


The maximum ambiguity rate for the development corpus (when all the possible tags of a word are kept) is equal to 2.4.

errors of the supertagger (almost one out of 5 words is attributed the wrong supertag) often impede the parsing stage.


The first one is the standard search of documents containing the words (lemmas) of the query or synonyms.

when the user clicks on one of these links, a new query is submitted to the engine.


In a perfect world, a query divides the document space in two parts, the relevant and non-relevant documents.


resulting in the failure to link real term variants).

in text, however, concepts are frequently denoted by different surface realisations of preferred terms, which we denote as their term variants.


Still, using only the patterns from Table 3, we have correctly extracted 35.76% of all GENIA coordinated terms, with more than a half of all suggested candidates being found among those that appeared exclusively in coordinations.

we show that ATR precision is enhanced by considering joint termhoods of all term variants, while recall benefits by the introduction of new candidates through consideration of different variation types.


The incorporation of acronyms had a significant high positive effect, in particular on more frequent terms (since acronyms are introduced for terms that are used more frequently).

the main reason for doing this is to avoid the distribution of frequencies of occurrence of term candidates across different variants, as these frequencies have a significant impact on estimating termhoods.


Among the most highly weighted features we found a mix of different feature types.

we produced artificial documents of 20 sentences in length from these sets of sentences.


Support vector machines are very robust to overfitting, and in our experiments we find that classification results were quite robust to feature vectors with up to 4 times the size of the training set.

modern machine learning techniques like support vector machines allow us to explore large feature vectors, combining these different feature sets to achieve high classification accuracy in style-based tasks.


the past research of (Fung and Yee, 1998;Rapp, 1995;Rapp, 1999) utilized context information alone and was evaluated on different corpora from ours, so it is difficult to directly compare our current results with theirs.


In the remainder of the paper, we first define paths in dependency trees.

(Koehn et al 2003) found that it is actually harmful to restrict phrases to constituents in parse trees, because the restriction would cause the system to miss many reliable translations, such as the correspondence between "there is" in English and "es gibt" ("it gives") in German.


To overcome this problem, we propose an acquisition process in which all the arguments are acquired separately.

in this paper, we propose a knowledge rich approach to paraphrase acquisition.


Recent researches in NLP have promoted a now widely-accepted shallow-based analysis framework that has proven to be efficient for a number of tasks, including information extraction and question answering.

moreover, existing structured knowledge contained in dictionaries, thesauri or semantic networks can boost the learning process by providing clear intuition over text units.


In the extrinsic approach, the evaluation measure is the extent to which a summary improves the efficiency of a specific task (e.g., relevance judgment in text retrieval).

hand-crafted encyclopedias lack new terms and new definitions for existing terms, and thus the quantity problem is crucial.


First, the simple sentences that match with a pattern are classified into the associated viewpoint group.

we selected the following 12 viewpoints for which typical expressions can be coded manually: definition, abbreviation, exemplification, purpose, synonym, reference, product, advantage, drawback, history, component, function.


In Figure 1, three paragraphs describing "XML" are presented with the titles of their source pages.

the third column denotes the compression ratio.


In the case of "#Reps=3", the average summary size was 1309 characters, which is almost the maximum size for a single description in handcrafted encyclopedias.

even if we focus on the 28 viewpoints, the coverage of the VBS method can potentially increase.


The goal of our research is to automatically compile a high-quality large encyclopedic corpus using the Web.

the implementation of each step varies depending on the application.


We elaborate on the four steps in Sections 3.2-3.5, respectively.

while the expected answer for a TREC question is short definition sentences as in a dictionary, we intend to produce an encyclopedic text describing a target term from multiple viewpoints.


For that purpose, most past work (Appelt, 1985;Dale and Haddock, 1991;Dale, 1992; Dale and Reiter, 1995;Heeman and Hirst, 1995;Horacek, 1997;Krahmer and Theune, 2002;van Deemter, 2002;Krahmer et al., 2003) makes use of attributes of an intended object (the target) and binary relations between the target and others (distractors) to distinguish the target from distractors.

it will be able to handle not only intra-group relations (e.g., the edges with labels "front", "middle", and "back" in Figure 4) but also inter-group relations (e.g., the edge from "Group 1" to "Table" in Figure 4).


12.2 evaluations were obtained for each expression on average.

1 "to the right of " is not a discriminatory relation, for objects a and c are also located to the right of the table.


In order to condition on another feature of each ordered daughter i D , such as its semantic relation i ψ to the head constituent H, we also first predict it, according to the chain rule.

let the number of moves , since insertions and deletions can be paired uniquely 3.


4 "j" and "at" are the tags representing connecting and tense/aspect roles respectively.

among them, 500 are used as training data, and 200 as test data, which are partitioned equally into two sets.


Assuming event classes are independent of temporal indicators given c , we have:   A Naïve Bayesian Classifier assumes strict independence among all attributes.

whilst, a durative event, e.g., 盖楼 (built a house), is more complex and its accomplishment as a whole involves a process spreading in time.


A set of experiments are conducted to investigate the impacts of each individual feature type and the impacts when they are used in combination (shown in Table  3).

the order of two events in CTS does not necessarily correspond to the order imposed by the interpretation of the connective (Dorr, 2002).


In addition, it is very hard to detect orthographic variants caused by misspelling with ordinary rule-based methods, because preparing such rules for misspellings that might be writ-ten is an unrealistic approach.

one is based on the theory of the Kunrei (Kunreisiki) system.


We also implement the NCM model under DOM for reference.

we evaluate our method through several experiments for two language pairs: English/Chinese and English/Japanese.


As for SCF acquisition, the cooccurrence of one predefined SCF scf i with one verb v is the relevant primitive event, and the concerned probability is p(v|scfi) here.

the MLE method is closely related to the general performance of the concerned SCF acquisition system.


Using the pseudo word associations, a bilingual-dictionary-adaptation method selects translation equivalents for the pseudo target word.

the first step is the same as that of the contextual-similarity-based method described in Section 2.


The correlation between the i-th translation equivalent of target word x, denoted as y(i), and the j-th associated word x'(j) is defined as where MI(x, x'(j)) is the mutual information between x and x'(j), and PL(y(i), x'(j)) is the plausibility factor for y(i) given by x'(j).

the total number of selected target words was 12,848.


Tanaka and Iwasaki's (1996) optimization method for a translation-probability matrix mainly aims at adapting a bilingual dictionary to domains.

under the assumption that target word x and candidate translation equivalent y are characterized by first-language context vector a(x) = (a 1 (x), a 2 (x), …, a m (x)) and second-language context vector b(y) = (b 1 (y), b 2 (y), …, b n (y)), respectively, b(y) is translated into a first-language vector denoted as a'(y) = (a' 1 (y), where δ i,j =1 if the j-th element of b(y) is a translation of the i-th element of a(x); otherwise, δ i,j =0.


Using these pseudo target words, both the CS and RAW methods were evaluated.

contextual similarity is ineffective for a target word with two or more senses occurring in a corpus.


Preparing a term vector of the precedent sentences, we calculate how much the precedent content is covered by other sentences using distance defined above.

spearman's rank correlation τ s and Kendall's rank correlation τ k failed to prove the significant difference between CO, PO and HO.


Including an anaphora resolution component would also result in better Recall.

we used Gold Standard 2 in the evaluation reported in Section 5.


In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) with linguistic information.

: we enforce that each parameter will be distributed according to a Gaussian prior with mean µ and variance σ 2 .


1 (as in He woke up or in Suddenly Eleni woke up).

the structure is passed on into the graphical-visual domain, whereas the contents are kept in linearized form.


One promising possibility to speed up the lexical acquisition process is to glean the semantics of words from a corpus by adopting the cooccurrence model of word meaning.

we find that discriminative and characteristic weighting procedures are able to identify different kinds of features useful for learning a classifier, both consistently enhancing the classification accuracy.


They determine w(f,c) from the distribution of f between c and c , attributing greater weights to those f that correlate with c or c most.

both TS and TSL 1 require computation of similarities between a large set of words and thus incur significant computational costs.


Only those classes were used in the study that had 5 or more members.

we experimented with several similarity metrics and found that on both datasets Jensen-Shannon Divergence yields the best classification results (see Table 1).


This tagger treats an unknown word that did not appear in the training corpus as a noun.

the noun lists from the authors' corpora are extracted based on the similarities between the target's NFV in the entire corpus and the NFVs in each authors' corpora.


Taking into account, on the one hand, the outstanding importance of Spanish as a major Western  (column 1) is orthographically transformed (column 2), segmented according to the subword lexicon (column 3), while content-bearing subwords are mapped to MSI-specific equivalence classes whose identifiers (MIDs) are automatically generated by the system (column 4).

we restrict ourselves to the specific sublanguage used in the context of the medical domain.


We then used the local context in language-specific corpora in order to validate these cognate pairs.

in our experiments, we intended to express the degree of similarity given a pair of cognates.


For instance words such as "Mensch" (human being),a "Gast" (guest), "Flüchtling" (refugee) are masculine in gender, yet there is no corresponding female form in German.

b) We do not mark articles and adjectives which preceed the marked noun.


c) It is currently impossible to look beyond the sentence boundary.

the components build up on each other's output.


In example (5), the nominalized plural adjective "Sachverständige" (experts) is ambiguous with respect to gender.

in a first annotation schema we assigned more than 30 different error codes literally taken from (Ges, 1999) and (Uni, 2000).


For example, a personal name and its transliterated word are shown below.


Using the pronunciation of a dictionary or limiting output English words to a particular English word list prepared in advance can simplify the problem of back transliteration.


Much research is also being directed at acquiring affect lexica automatically Littman 2002).

amongst the obviously "affect"-charged terms and features in the top 100 are: +Neg 1 , unable to, thanks, the good, easy to, ease of, lack of, not find, not work, no help, much accurate, a simple there are many features that carry high weights, but are not what one would intuitively think of as a typical affect indicator: try the, of, off, ++Univ 2 , aDV PRON PREP 3 , NP::PRON:CHaR 4 , @@adj Props Verb Tsub Pron 5 , aUXP::VERB, your We conclude from this inspection of individual features that within a specific domain it is not necessarily advisable to start out with a resource that has been geared towards containing particularly affect-charged terminology.


The word "officeholders" was misrecognized due to the    repetition of articles.

in this paper we described a novel way to discover Named Entities by using the time series distribution   Figure 3: Relationship of the score and the likelihood of being a Named Entity (Multi-word).


For each word w which appeared in newspaper A, we got the document frequency at date t: where df A (w, t) is the number of documents which contain the word w at date t in newspaper A.

normally a named Entity tagger uses lexical or contextual knowledge to spot names which appear in documents.


Then, sentences in our test corpus are treated as chunk sequences.

during the construction of an information extraction (IE) system in the biomedical domain, we found that not only the task of named entity recognition (NER), but also the appropriate handling of verbs in this domain plays an important role.


In order to avoid accidental errors during the process (mainly the wrong assignment of POS tags), the MWV candidates with the lowest frequencies from 1 to 4 are out of consideration.

a variation of finite state automaton is considered in our approach for the extraction of MWVs.


We demonstrate an average of 8-10% more recall using the first approach.

their tests were made on a closer language pair (English -French).


The question-answer detection system we present will ultimately serve as one component of a full email summarization system, providing a portion of summary content.

this task is not as complicated a task as automatic question answering where the search space for candidate answers is much wider and more sophisticated measures than those based on lexical similarity have to be employed.


Text simplification is a useful task for varied reasons.

we evaluated our summarizer on the DUC test sets using the Rouge automatic scoring metric (Lin and Hovy, 2003).


We now describe our experiments on syntactic simplification and sentence clustering.

the syntactic simplification literature deals with syntactic constructs that can be interpreted from a rhetorical perspective.


The difference between those methods and ours is the way we use information derived from the dictionary for WSD.

we mention some related works in Section 6, and conclude the paper in Section 7.


Most current research uses machine learning techniques (Li and Takeuchi, 1997;Murata et al., 2001;Takamura et al., 2001), has achieved good performance.

• SVM+NB(simple) The combined model by simple ensemble (Subsection 4.1).


Furthermore, many extraction patterns for the EDR concept dictionary may also be applicable for other dictionaries.

these various methods are not in competition with our method.


State of the Art Previous computational concerns for economy of grammatical representation have given way to models of language that not only exploit generative grammatical resources but also have access to large lists of contexts of linguistic items (words), to which new structures can be compared in new usages.

in its present form, at least, FrameNet has at least as many gaps as senses.


Hence, "firing| " is also chosen as a valid category and the concepts in this category are linked to the "beat.v" lexical entry in the "cause_harm" frame.

compounded by the ambiguity of word boundaries, many predicate usages in chinese are highly unexpected.


The final output of AHRAI is the top k pairs in this ranking after some heuristic rules are applied to it in Step 4.

the results suggest that our method can acquire a significant number of hyponymy relations that the alternatives miss.


The number of candidates in the difference regions, N D 1 and N D 2 , may be small, especially for acceptance regions with large overlap (this was one of the reasons for using conditional inference rather than a normal approximation in Section 3.1).

a first step in the search for an appropriate significance test is to formulate a (plausible) model for random variation in the evaluation results.


It is obvious that a repetition of the evaluation experiment may lead to quite different precision values, especially for n < 1 000.

significant differences between the G 2 and X 2 measures according to Fisher's test (at a 95% confidence level) are marked by grey triangles.


There is much confusion about their validity, though, mainly due to the fact that assumptions behind the application of a test are seldom made explicit.

it is advisable to use Fisher's exact test (Agresti, 1990, 60-66) instead of an asymptotic test that relies on large-sample approximations.


(1999) involves repeated selection of cut-off thresholds in order to compute precision and recall on a test set that contains equal number of positive and negative examples of confusable drug name pairs.

a slight variation of this measure is obtained by adding extra symbols, such as spaces, before and/or after each string (Lambert et al., 1999).


An analysis of the reasons behind the unsatisfactory performance of commonly used measures led us to propose a new measure of orthographic similarity: BI-SIM.

it returns 1 if and only if the strings are identical, and 0 if and only if the strings have no segments in common.


on average more than one for each English cluster.

3We then combine the two most similar documents into a cluster, for which we calculate a new representation by merging the two vectors into one.


In fact, all features have only very little influence.

in order to create a reference frame linking this figure to the following ones, we add a second line, along the core of the cluster of the LOCNESS texts.


These, we expected, were likely to be rather abundant, while they would have been subjected to a relatively low degree of editing.

the further the texts are plotted to the right, the more similar their profile is to the mean profile for the A-level training texts.


While these association measures have their statistical merits in collocation identification, it is interesting to note that they have relatively little to do with the linguistic properties (such as those mentioned at the beginning) which are typically associated with the notion of collocativity.

secondly, our kind of syntactic preprocessing (which is standard nowadays) allows collocation extraction algorithms to better control the structural types of collocations.


Our method for extracting relevant keywords consists of the following two steps.

the extracted sentences are greatly changed in the case of "long" when a user had changed relevant keywords.


To examine the assumption, the results were further analyzed with the frequency of the test set words.

the difference between two numbers is just 100.


The SO of a word often conforms to that of its morphemes.

lexical types should be carefully considered in the algorithm for Chinese because Chinese is an isolating language -no form change.


However, the recall drastically increases from 45.56%, or 59.57% under the 40-word condition, to 85.03%.

it is noted that morphemes are much less numerous than words, and that also a small number of fundamental morphemes may be used in the modified system to great advantage.


For example, the second row (starting with 75%) in table 1 shows the precision when the top 75% were classified and the last 25% (with lowest confidence) were ignored.

it is impossible to construct a word like '*ful-care', '*less-care'.


Further, in the majority of cases, it tends to be more frequent than its hyponyms and less frequent than its hypernyms.

the first approach is not ideal since it assumes that the goal of distributional similarity methods is to predict semantic similarity and that the semantic resource used is a valid gold standard.


Instead of using arbitrary windows to compute word similarity measures, we model lexical affinity using the complete observed distance distribution along with independence and parametric models for this distribution.

we want to scan the whole corpus efficiently in order to make this framework usable.


Previous research has shown the difficulty to describe the cognitive aspects of user behavior.

we base our approach on the work done on Plasticity of User interfaces (Thevenin and Coutaz, 1999) and the tool ART-Studio (Calvary et al., 2001).


One limitation of the corpus-based translation knowledge acquisition approach is that the techniques of translation knowledge acquisition heavily rely on availability of parallel/comparative corpora.

considering such a situation, this paper proposes to complementarily use much larger monolingual Web documents collected by search engines, as a resource for reliably re-estimating bilingual term correspondences.


This pattern (an instance of the gloss+hypeonymyr/meronymy rule) cumulates evidence for senses #1 of both railway and company.

as far as the context T is concerned, the intuition is that, with a larger T , there are higher chances of detecting semantic patterns among the "correct" senses of the terms in T. the dimension of contexts T i is an external contingency, it depends upon the available corpus.


Occurrence frequencies of bilingual word pairs are similar; 6.

a parallel corpus is a sentence-aligned corpus containing bilingual translations of the same document.


Key to this process is efficient resegmentation of the corpus, which entails incremental update of the description length change that each prefix p will give upon resegmentation, denoted ∆CODE p (the change in the coding cost CODE(M, Data) for the corpus plus the model M ).

rather than segmenting prefixes and suffixes separately, the data structures and algorithms should be extended to segment both prefixes and suffixes in the current morph list, depending on which gives the best overall DL improvement.


The parsing result is a phrase structure in terms of bracketed annotation.

such constraints, during the phase of target language generation, can help in determining the order in producing the translation for an intermediate constituent unit from its subtrees when the corresponding translation of the unit is not associated in the TCT representation.


The search starts from 15-precedente and ends after the first extension of the text window as sogno can be found in position p-1.

in all our experiments with GiZA++ we adopted a configuration of the system which is reported by the authors to produce optimal results, that is 1 5 H 5 3 4 4 4 5 4 , where the number in the base refers to the iBM models 1, 3, 4, and 5, H refers to the HMM training, and the superscript figures refer to the number of iterations.


In the current phase of the project, only 2,948 sentences are available.

this is even clearer if we consider the extended versions of KNOWA, as reported in table 8.


Whereas in the Hansard corpus the texts are direct translations of each other, in the EuroCor corpus it happens quite frequently that the English and Italian texts are both translation of a text in a third language.

in this paper we present KNOWA, an English/italian word aligner, developed at iTC-irst, which relies mostly on information contained in bilingual dictionaries.


Adequate confirmation for keywords is indispensable in spoken dialogue systems to eliminate misunderstandings caused by speech recognition errors.

we use a syntactic parser KNP (Kurohashi and Nagao, 1994) to divide the ASR results into the phrases 2 .


On the other hand, if a critical error occurs in the ASR result, such as those in the product name in software support, the following retrieval would make no sense.

the significance score is calculated for portions that are different among N-best candidates.


We use a syntactic parser KNP (Kurohashi and Nagao, 1994) to divide the ASR results into the phrases 2 .

it is not feasible to define such keywords in retrieval for operation manuals (Komatani et al., 2002) or WWW pages, where the target of retrieval is not organized and is written as natural language text.


These values were used in the following experiments.

an F-measure of 84.9 was achieved for the accuracy of sentence boundary detection by using the proposed methods.


To train the language model represented by P (Y ), we used the transcriptions of 178 talks excluding the test data.

so in this paper, we mainly focus on this problem and describe our solution to it.


These were developed using a database of 30 2-3 phrase fragments from newspaper stories all of which have been successfully interpreted by the program.

the criteria employed favor constructions that have simple descriptions (relative to the available meaning representations and current set of constructions) and are frequently employed.


We therefore expect agreement between the clustering solution and GS to be higher than the agreement with a classification based on morphological class.

also thanks to Nadjet Bouayad, Katrin Erk and 5 anonymous reviewers for revision and criticism of previous versions of the paper.


The morphological mapping works best for nonderived adjectives: 14 out of 16 were basic in denotation (the remaining two were classified as object).

for the same reason, they will appear in postnominal position when acting as modifiers.


The behaviour of the objects in cluster 0 (the biggest cluster by far) presents more cohesion than that of the objects in cluster 1, which have a medium mean value for most features.

merlo and Stevenson (2001) report inter-judge values of 0.53 to 0.66 for a task we consider to be comparable to ours, that of classifying verbs into unergative, unaccusative and object-drop, and argue that Carletta's "is too stringent a scale for our task, which is qualitatively quite different from content analysis" (merlo and Stevenson, 2001, 396).


The lowest agreement scores are those of J2, the only judge who had not done research on adjectives.

our hypothesis seems to be backed up by the data available.


Alignment usually is performed when the ontologies cover domains that are complementary to each other.

they are disappointing in ontology integration, especial when the original ontologies are well defined.


To demonstrate how the implemented system works, we will consider the normalization of the following description in English of a command for the Unix shell with the grammar of figure 3: List all files.

a naive way to select the candidate content representation found by the system that best corresponds to the input document would be to show to an expert all the normalized texts corresponding to the candidates.


Such a view shows the normalized text corresponding to all the objects from the root object that are not in competition.

the expert can choose to resolve first underspecifications that contain likely objects, or underspecifications that involve few candidates so that the validation of an object will prune more candidates from the compact representations.


Even tri-gram model suffers from sparse-data estimation problem, but various smoothing techniques (Goodman, 2001) have led to significant improvements in many applications.

lSA is better at predicting topic bearing content words while parsing based models are better for function words.


Even n-gram models are quite better at modeling function words, but they lack the large-span semantic that can be achieved by LSA.

table 2: Perplexity without content/function word knowledge.


We also used the supertagged test corpus, thus we knew the supertag of the next word being predicted.

given a supertag belonging to function word specific supertags, the 'vocabulary' for the target word is reduced by orders of magnitude compared to the case for content word specific supertags.


Finding a global optimum is usually difficult in a high dimensional vector space.

the statistics of corpus is shown in table 1.


The performance improvement was confirmed by consistent experimental results and measured by using various objective translation metrics.

• Part-of-speech language models: English part-of-speech language models were used.


This allows us to incorporate descriptions of objects to be excluded, to produce enumerations and compositions of descriptions of subsets of the intended referents, and to build compositions of increasingly restricting descriptions of these referents.

we have enhanced this repertoire by a complexity cut-off, carried out prior to further expanding a node if the boolean combination of descriptors build leads to a description that is more complex than a given threshold.


2003), with limited coverage, since only few attributes typically apply to all intended referents and to none of the potential distractors.

this yields (car ^ right) ∨ (white ^ right), which is a possible solution.


In order to compare the effectiveness of the cut-off techniques, we have run the same sample of 100 cases (50 with and 50 without size and age), with all combinations of at least one cut-off technique.

van Deemter (2002) has extended the set of descriptors to boolean combinations of attributes, including negations.


We have looked how the algorithm behaves when we used both nouns and verbs in the graph of senses.

in a HSW, this feature is typically high.


Experimental results of indirect anaphora resolution also indicated the effectiveness of our approach.

first of all, we briefly introduce NTT Semantic feature Dictionary employed in this paper.


Experimental evaluations emphasize the importance of pre-identifying syllables correctly, show the accuracy of applying PPM to syllable segmentation of 98%, and indicate the effectiveness of the proposed approach to combine syllables into words.

a recognized effective approach to word segmentation is Longest Matching, a method based on dictionary.


If, on the other hand, a context of length q, 0<=q <=k, is found, then the probability of this next character is estimated to be the product of probabilities of escape characters at levels k, k-1, ..., q+1 multiplied by the probability for the context found at the q-th level.


At step 3, our system selects variant pairs from the candidate pairs using a semantic similarity which is calculated by a vector space model of a context of each KATAKANA word.


Furthermore, they account for a relatively small percentage of tokens for the filtered words as shown by ¥ S ¦ 6 g ¢ ) h 6 h 9 .

we describe some related work in section 5 and conclude in section 6.


Much about the behaviour of words is most appropriately expressed in terms of word senses rather than word forms.

this method uses a ranking of senses derived automatically from raw text using both distributional similarity methods and a measure of semantic similarity, such as those available in the WordNet similarity package.


For each noun we considered the co-occurring verbs in the direct object and subject relation, the modifying nouns in noun-noun relations and the modifying adjectives in adjective-noun relations.

an NLP application computing over word senses is faced with considerable extra ambiguity.


During both phases, we get n-best lists with Viterbi search, and finally choose the best output with re-ranking method.

table 4 shows the performance for test set.


Hence we report coverage figures in this paper, and repeat figures for dependency recovery from an earlier paper.

a CCG supertagger (Clark and Curran, 2004a) is used to assign the categories.


overall, our new model offers three contributions.


In this context, as we saw with Figure 4, context makes it clear that any response to draw a line underneath must draw the mouth.

we have implemented FIGLET in Prolog using CLP(R) real constraints (Jaffar and Lassez, 1987) for metric and spatial reasoning.


Because of this, we annotated the CU Children's Read Story Corpus in terms of a set of event labels which we feel might have most relation to speech recognition error rate.

while the type of feedback and level of feedback is the current subject of much debate within the research community, recent results have shown that automated reading tutors can improve student achievement (Mostow et al., 2003).


Then each constituent is classified into one of several semantic roles using a number of features derived from the sentence structure or a linguistic context defined for the constituent token.

for example, a leaf member with det dependency relation is not considered for semantic labeling.


In the experiments, the gold standard constituency parses were used.

in dependency tree representations, we observe that the semantic roles are highly localized with respect to the chosen predicate.


Given the many possible forms of analytical questions, it would be counter-productive to restrict them to a predefined number of question/answer types.

fIGURE 8: A 2-conflict frame against the Iraq/uranium question that generated the dialogue in figure 7.


As a consequence, the maximum number of eigenvectors in the supervised KPCA model is the minimum of the number of features and the number of vectors from the labeled training corpus, while the maximum number of eigenvectors for the semi-supervised KPCA model is the minimum of the number of features and total number of vectors from the combined labeled and unlabeled training corpora.

assume a training corpus of the eight sentences as shown in Table 2, adapted from Senseval-2 English lexical sample corpus.


If we are sufficiently confident about the prediction made by the supervised KPCA model as to the predicted sense for the target word, we need not resort to the semisupervised KPCA method.

to obtain the sense predictions for test instances, we need only transform the corresponding vectors using the trained KPCA model and classify the resultant vectors using nearest neighbors.


in practice the labeled training corpus for WSD is typically relatively small, and does not yield enough training instances to reliably extract dependencies between features.


In particular, the collection Chemical-CompoundTypeByChemicalSpecies is known to be a specialization of the collection ExistingStuffType, via the transitive properties of genls.

a subset of the Cyc KB including parts of the English lexicon has been made freely available as part of OpenCyc (www.opencyc.org).


Indeed, having k+1 languages in our corpus, we need only apply the WSD method to the aligned wordnets for the target language (English in our case) and one source language, say L i , and alignment lexicons from the target language to every other language in the corpus.

the wordnets are aligned to the Princeton Wordnet (PWN2.0), taken as an interlingual index, following the principles established by the EuroWordNet consortium.


Instead of deriving the clustering tree and guessing at a perfect cut, we stop the clustering algorithm when Z clusters have been created, where Z is the number of senses in which the occurrences of TW i have been used in the text in question.

considering the fine granularity of the PWN2.0 sense inventory, our disambiguation results using parallel resources are superior to the state of the art in monolingual WSD (with the same sense inventory).


19.4 20.8 optimal choice 56.2 63.7 Table 4: Average precision of discovered senses for English in relation with WordNet As expected, the results obtained with first order cooccurrences (LAT-1.no), which produce a higher number of senses by word, are lower than the results obtained with second order cooccurrences (LAT-2.no).

the discovering of word senses is performed word by word and the processing of a word only relies on the subgraph that contains its cooccurrents.


Therefore, if a compound can be validated externally but not internally, it suggests that the concept may by P-Creative.


These compounds are undoubtedly included for a reason, yet the idea that literal compounds might actually be essential to WordNet's usefulness may strike some as heretical on at least two fronts: first, the lexicon is a finite resource, while the space of compounds is potentially infinite; and at any rate, literal compounds can be created as needed from purely compositional principles (Hanks, 2004).

it follows then that the novel compounds apple-pizza, chocolate-pizza, taco-pizza, and curry-pizza will all be internally validated as meaningful (if not necessarily enjoyable) varieties of pizza.


It follows then that the novel compounds apple-pizza, chocolate-pizza, taco-pizza, and curry-pizza will all be internally validated as meaningful (if not necessarily enjoyable) varieties of pizza.

the new concepts methanol-bomb and butanol-bomb can be generated, and from this the creative concept alcohol-bomb can be generalized.


In these examples the angles are actually the elements of the linear pair.

some cases, like that of passive versus active constructs, can be taken care of in the grammar.


The PropBank project (Kingsbury and Palmer, 2002) provides a large human-annotated corpus of semantic verb-argument relations.

we encode the constraints as linear inequalities, and use integer linear programming(ILP) as an inference procedure to make a final decision that is both consistent with the constraints and most likely according to the learning system.


P (u i )))) where N P denotes the lambda-expression given from the LPP-embedded NP, Loc P rep is the lambda-expression of locative preposition given above and u i the reference marker introduced by the LPP.

we have to account for the coreference link between pronoun "y" and a 'place', described by the antecedent LPP.


4 Using a classifier that returns confidence scores instead of binary decisions is crucial, because the training vectors that correspond to definitions are much fewer than the vectors for non-definitions (3004 vs. 15469 in our dataset of section 4).

"It is highly unlikely that moving one or a few genes from a pathogen to… …definite intraspecial physiological and morphological diversity.


Although relatively easy task for people, detecting an opinion holder is not simple either.

for instance, "afraid" with strength -0.99 represents strong negavitity while "abysmal" with strength -0.61 represents weaker negativity.


if Prolog fails to prove the formula with its current knowledge, then negate the formula and try to prove this negation.


Resolving an anaphor is the job of finding out which discourse marker (unique internal name) to use for the entity referred to by this phrase in the knowledge base.

a single semantic formula usually corresponds to several different sentences, even without taking synonymity of words into account.


Although this study directly compared the HPSG output with PropBank, we may require an additional machine learning step as in the existing studies to obtain higher accuracy because the accuracy attained by gold parses showed a limitation of our approach.

since the PropBank has been developed independently of any grammar formalisms, the results are comparable with other published results using the same test data.


We prepared two grammars for the evaluation: ¡ penn was extracted from the Penn Treebank with the original algorithm (Miyao et al., 2004), and ¡ prop was extracted using the PropBank annotations for argument/modifier distinction by a method similar to Chen and Rambow (2003).

firstly, given a parse tree (the top of the figure), we annotate partial specifications on an HPSG derivation (the middle).


This result reveals the accurate identification of predicate-argument relations by HPSG parsing.

lexical alternations and arbitrariness of assignments of argument labels will be a problem when we directly compare the output of an HPSG parser with the PropBank.


For example, using a modified lexical weighting, the systems are ranked according to their BLEU scores as follows: S B , S A , S G , S I , S U -an ordering that differs from that of AER but is identical to that of CPER (with a phrase length of 3) for Chinese.

we designed CPER, a new phrase-oriented metric that is more informative than AER when the alignments are used in a phrase-based MT system because it is an indicator of how the set of phrases differ from one alignment to the next according to a pre-specified maximum phrase length.


CPER is similar to AER but operates at the phrase level instead of at the word level.

loose phrases containing NULL-aligned words along their edges receive the same lexical weighting as tight phrases without NULL-aligned words along the edges.


We then present several experiments to measure the impact of different word alignments on a phrase-based MT system, and investigate how different alignments change the phrase selection in the same MT system.

they cover a higher percentage of our test sets and result in fewer untranslated words and selection of longer phrases during decoding.


The hierarchical structure of our topic segmentation model allows variation in content to be expressed at two levels, with each segment being produced from a linear combination of the distributions associated with each topic.

future Work Spoken discourse exhibits several features not derived from the words themselves but which seem intuitively useful for segmentation, e.g.


We first determine the impact of long-range pairwise similarity dependencies on segmentation performance.

when dependencies up to a hundred sentences are considered, the algorithm yields a 25% reduction in P k measure.


We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion.

we want to find the segmentation with a maximally homogeneous set of segments that are also maxi-mally different from each other.


Without a correct parse, we cannot rule out the link between her and Alex through Binding Theory.

path coreference is undefined for cases where there is no path between the pro-noun and the candidate noun -for example, when the candidate is in the previous sentence.


Section 2 gives some related work that utilizes the structured syntactic knowledge to do pronoun resolution.

the last line of table 5 shows the results by adding these features into the normal feature set.


Min-Expansion could, to some degree, describe the syntactic relationships between the candidate and pronoun.

the candidate with the highest confidence value will be selected as the antecedent of the pronominal anaphor.


These features are calculated by mining the parse trees, and then could be used for resolution by using manually designed rules (Lappin and Leass, 1994;Kennedy and Boguraev, 1996;Mitkov, 1998), or using machine-learning methods (Aone and Bennett, 1995;Yang et al., 2004;Luo and Zitouni, 2005).

to our knowledge, the application of such a technique to the pronoun resolution task still remains unexplored.


as a baseline test case of the tagger, the well-established asymmetry between subject-and object-relative clauses was tested as shown in (11).


Instead, "on" and "to", have different frequencies and this information is reflected in the conditional probability P (word i |state).

we do not deny the limitations of finite-state models, but argue that our results show that their usefulness has been underestimated.


Preposition) and the POS sequences for both sentences are identical.

the findings in the current study play out differently.


When a syntactic structure can be associated to a sentence, according to a given grammar, we can decide whether or not the sentence is grammatical.

mary took very long way a for the return.


Such capabilities render it possible to arrive at a precise evaluation of the grammaticality of the input.

the grammaticality index is a little bit higher because a lot of constraints are also satisfied.


Special thanks to Miles Osborne, Steven Bird, Timothy Baldwin and the anonymous reviewers for their feedback and insightful comments.

for our french-English language pair we POS tagged the source and target sentences with Tree-Tagger.


Our work lies between these two directions; we attempt to discover parallelism at the level of fragments, which are longer than one word but shorter than a document.

training on both datasets together brings further improvements.


For each of the linked target words, the corresponding signal value is the probability of the link (there can be at most one link for each target word).

we perform experiments in the context of Romanian to English machine translation.


(2004) provided a partial solution by describing a method to predict the predominant sense, or the most frequent sense, of a word in a corpus.


In addition, our parallel texts training data represents a natural domain difference with the test data of SENSEVAL-2 English lexical sample task, of which 91% is drawn from the British National Corpus (BNC).

using a learning algorithm which already gives well calibrated posterior probabilities may be more effective in estimating the sense priors.


The predominant senses are automatically acquired from raw text without recourse to manually annotated data.

sense similarity was computed using the Lesk's (Banerjee and Pedersen, 2003) similarity measure 1 .


Each corpus is pre-processed using the Alembic Workbench POS-tagger (Day et al.


In order to justify the usefulness of our hierarchical learning strategy when a rough class hierarchy is not available and difficult to determine manually, we also experiment using entirely automatically built class hierarchy (using the traditional binary hierarchical clustering algorithm and the cosine similarity measurement) without considering the existing class hierarchy.

typical works include Miller et al (2000), Zelenko et al (2003), Culotta and Sorensen (2004), Bunescu and Mooney (2005a), Bunescu and Mooney (2005b), Zhang et al (2005), Roth and Yih (2002), Kambhatla (2004), Zhao and Grisman (2005) and Zhou et al (2005).


The "0" tag means that the word is not in any chunk.

in the label propagation process, the soft label of each initial labeled example is clamped in each iteration to replenish label sources from these labeled data.


The PUG formalism is extremely simple: it only imposes that combining two structures involves at least the unification of two objects.

(2004), who show advantages of negative and positive polarities for prefiltration in parsing (a set of structures bearing negative and positive polarities can only be reduced into a neutral structure if the sum of negative polarities of each object type is equal the sum of positive polarities).


Anonymous nodes are assigned types only after all modules combine, see section 7.1.

we assume familiarity with theories of (typed) unification grammars, as formulated by, e.g., Carpenter (1992) and Penn (2000).


To improve the accuracy of longsentence parsing, we thought that it would be more effective to cyclopaedically divide a sentence into all types of clauses and then parse the local dependency structure of each clause.

in order to resolve the sparse data problems caused by estimating P (b i k rel → b i l |B i ) with formula (2), we adopted the smoothing method described by Fujio and Matsumoto (Fujio and Matsumoto, 1998) Here, the head bunsetsu of the final bunsetsu of a clause boundary unit is identified.


A dependency is a modification relation in which a dependent bunsetsu depends on a head bunsetsu.

to evaluate the effectiveness of our method for Japanese spoken monologue, we conducted an experiment on dependency parsing of the spoken monologue sentences recorded in the "Asu-Wo-Yomu."


The average time taken for clause boundary analysis was about 1.2 millisecond per sentence.

in this paper, we describe a sequence of clause boundary units in a sentence as  which is a set of dependency relations whose dependent bunsetsu is the final bunsetsu of each clause boundary unit in the input sentence.


Our previous work (Rotaru and Litman, 2005) had shown that certain SRP will correlate with emotional responses from the user.

if any of the two variables involved in a significant dependency has more than 2 possible values, we can look more deeply into this overall interaction by investigating how particular values interact with each other.


We find again only one significant interaction: rejections are followed by more emotional turns than expected ( Table 7).

in correct turns recognition problems are more likely to screw up the correctness interpretation (e.g.